Efficient 3D reconstruction of vessels from multi-views of X-Ray Angiography

In this paper, we present an efficient 3D vessels reconstruction algorithm based on multi-views of X-ray Angiography assisting interventional surgery. First, we extract the vascular-like structures from the image sequences using a geometrical analysis of multi-scale Hessian matrix eigen-system and use the fast marching method to extract the skeleton of the structure, from which we derive the vascular topological configurations. Second, we regard the 3D space as a Markov Random Field and formulate the reconstruction problem as an energy minimization problem with consistent, continuous and topological constraints to coarsely register and reconstruct the 3D vessels. Third, we refine the reconstructed vessels to register and reconstruct the 3D vessels accurately. We demonstrate our system in coronary arteries reconstruction for percutaneous coronary intervention surgery to help doctors learn about the configurations of the coronary arteries of specific patient during operation. We envision that our system will be used for clinic treatment to advance vessel reconstruction for diagnosis and therapy in the near future.